A time series is a sequence of data points, typically consisting of successive measurements made over a time interval. Time series data is used in a wide variety of applications, and is often analyzed to extract meaningful information about a time series and/or to forecast future events or time series values. For example, time series data may be analyzed using machine learning techniques. A variety of types of machine learning methods exist (e.g., linear regression model, naïve Bayes classifier). Machine learning is commonly used for addressing “Big Data” problems in which the volume, variety, and velocity of data are high and/or real time data processing may be desired. There are many different domains or modalities from which time series data can be collected, many types of time series data, and many different sources from which time series data may be collected or generated. Typically, for a particular method of time series analysis or machine learning, one or more specific types of time series data is input to produce the desired results, such as a detection or prediction of some characteristic or event.